Overview

Dataset statistics

Number of variables15
Number of observations2775
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory325.3 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Warnings

gross_revenue is highly correlated with qtde_itemsHigh correlation
qtde_items is highly correlated with gross_revenueHigh correlation
avg_ticket is highly skewed (γ1 = 27.68809767) Skewed
frequency_purchase is highly skewed (γ1 = 47.42232667) Skewed
df_index has unique values Unique
recency_days has 33 (1.2%) zeros Zeros
returns has 1483 (53.4%) zeros Zeros

Reproduction

Analysis started2021-06-28 22:41:49.028999
Analysis finished2021-06-28 22:42:22.970883
Duration33.94 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2775
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2248.812252
Minimum0
Maximum5694
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:23.135822image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile181.4
Q1895.5
median2055
Q33408.5
95-th percentile4956.3
Maximum5694
Range5694
Interquartile range (IQR)2513

Descriptive statistics

Standard deviation1526.103739
Coefficient of variation (CV)0.6786265673
Kurtosis-0.9555046207
Mean2248.812252
Median Absolute Deviation (MAD)1238
Skewness0.3811143644
Sum6240454
Variance2328992.623
MonotonicityStrictly increasing
2021-06-28T19:42:23.345696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
5991
 
< 0.1%
26381
 
< 0.1%
5911
 
< 0.1%
5931
 
< 0.1%
26421
 
< 0.1%
5951
 
< 0.1%
26441
 
< 0.1%
5971
 
< 0.1%
26461
 
< 0.1%
Other values (2765)2765
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
56941
< 0.1%
56841
< 0.1%
56781
< 0.1%
56531
< 0.1%
56471
< 0.1%

customer_id
Real number (ℝ≥0)

Distinct2767
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15277.96396
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:23.565472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12614.4
Q113804.5
median15240
Q316779.5
95-th percentile17950.3
Maximum18287
Range5940
Interquartile range (IQR)2975

Descriptive statistics

Standard deviation1721.261834
Coefficient of variation (CV)0.112663038
Kurtosis-1.211386655
Mean15277.96396
Median Absolute Deviation (MAD)1489
Skewness0.01595493513
Sum42396350
Variance2962742.301
MonotonicityNot monotonic
2021-06-28T19:42:23.782903image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123702
 
0.1%
124312
 
0.1%
124572
 
0.1%
123942
 
0.1%
124222
 
0.1%
124172
 
0.1%
124552
 
0.1%
124292
 
0.1%
163841
 
< 0.1%
150021
 
< 0.1%
Other values (2757)2757
99.4%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2762
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2845.153449
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:24.008120image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.566
Q1629.045
median1169.94
Q32427.565
95-th percentile7395.585
Maximum279138.02
Range279101.46
Interquartile range (IQR)1798.52

Descriptive statistics

Standard deviation10462.81725
Coefficient of variation (CV)3.677417558
Kurtosis373.090023
Mean2845.153449
Median Absolute Deviation (MAD)691.16
Skewness17.1046029
Sum7895300.82
Variance109470544.8
MonotonicityNot monotonic
2021-06-28T19:42:24.219162image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3226.12
 
0.1%
1080.482
 
0.1%
2339.862
 
0.1%
3425.692
 
0.1%
3312
 
0.1%
734.942
 
0.1%
6382.452
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2043.232
 
0.1%
Other values (2752)2755
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.57369369
Minimum0
Maximum372
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:24.388644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.34408861
Coefficient of variation (CV)1.208054206
Kurtosis3.456145254
Mean56.57369369
Median Absolute Deviation (MAD)23
Skewness1.902657391
Sum156992
Variance4670.914448
MonotonicityNot monotonic
2021-06-28T19:42:24.542365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
486
 
3.1%
386
 
3.1%
285
 
3.1%
876
 
2.7%
1067
 
2.4%
967
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (242)2027
73.0%
ValueCountFrequency (%)
033
 
1.2%
199
3.6%
285
3.1%
386
3.1%
486
3.1%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%

qtde_invoices
Real number (ℝ≥0)

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.05981982
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:24.692053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.070779717
Coefficient of variation (CV)1.496872842
Kurtosis183.9137519
Mean6.05981982
Median Absolute Deviation (MAD)2
Skewness10.62190999
Sum16816
Variance82.27904467
MonotonicityNot monotonic
2021-06-28T19:42:24.856393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2778
28.0%
3498
17.9%
4394
14.2%
5238
 
8.6%
6174
 
6.3%
7138
 
5.0%
897
 
3.5%
970
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)279
 
10.1%
ValueCountFrequency (%)
2778
28.0%
3498
17.9%
4394
14.2%
5238
 
8.6%
6174
 
6.3%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1634
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1672.284685
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:25.023057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119.7
Q1330.5
median707
Q31485.5
95-th percentile4610.5
Maximum196844
Range196842
Interquartile range (IQR)1155

Descriptive statistics

Standard deviation5888.405154
Coefficient of variation (CV)3.521173882
Kurtosis485.8647154
Mean1672.284685
Median Absolute Deviation (MAD)455
Skewness18.18482489
Sum4640590
Variance34673315.26
MonotonicityNot monotonic
2021-06-28T19:42:25.167535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
2468
 
0.3%
1508
 
0.3%
4937
 
0.3%
3007
 
0.3%
2727
 
0.3%
2197
 
0.3%
3947
 
0.3%
5167
 
0.3%
12007
 
0.3%
Other values (1624)2699
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%

qtde_products
Real number (ℝ≥0)

Distinct466
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.7556757
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:25.318882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median72
Q3143
95-th percentile399.9
Maximum7838
Range7836
Interquartile range (IQR)109

Descriptive statistics

Standard deviation277.7016109
Coefficient of variation (CV)2.140188546
Kurtosis337.1075724
Mean129.7556757
Median Absolute Deviation (MAD)45
Skewness15.35671564
Sum360072
Variance77118.1847
MonotonicityNot monotonic
2021-06-28T19:42:25.459784image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2838
 
1.4%
3534
 
1.2%
2630
 
1.1%
2930
 
1.1%
2730
 
1.1%
2528
 
1.0%
3127
 
1.0%
1527
 
1.0%
1927
 
1.0%
3326
 
0.9%
Other values (456)2478
89.3%
ValueCountFrequency (%)
211
0.4%
312
0.4%
416
0.6%
516
0.6%
624
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%

qtde_unique_products
Real number (ℝ≥0)

Distinct340
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.35495495
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:25.603776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q129
median57
Q3105
95-th percentile239.3
Maximum1786
Range1785
Interquartile range (IQR)76

Descriptive statistics

Standard deviation98.70296808
Coefficient of variation (CV)1.184128384
Kurtosis80.66815713
Mean83.35495495
Median Absolute Deviation (MAD)33
Skewness6.352949609
Sum231310
Variance9742.275908
MonotonicityNot monotonic
2021-06-28T19:42:25.759614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3738
 
1.4%
2437
 
1.3%
2636
 
1.3%
2535
 
1.3%
3334
 
1.2%
2834
 
1.2%
3032
 
1.2%
1832
 
1.2%
1530
 
1.1%
5229
 
1.0%
Other values (330)2438
87.9%
ValueCountFrequency (%)
119
0.7%
213
0.5%
317
0.6%
418
0.6%
523
0.8%
ValueCountFrequency (%)
17861
< 0.1%
17661
< 0.1%
13221
< 0.1%
11181
< 0.1%
8841
< 0.1%

avg_ticket
Real number (ℝ≥0)

SKEWED

Distinct2767
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.10453979
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:25.957086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.852950497
Q112.43090098
median17.94081081
Q325.10118182
95-th percentile87.67205263
Maximum4453.43
Range4451.279412
Interquartile range (IQR)12.67028084

Descriptive statistics

Standard deviation107.5928583
Coefficient of variation (CV)3.351328474
Kurtosis1055.381089
Mean32.10453979
Median Absolute Deviation (MAD)6.342522523
Skewness27.68809767
Sum89090.09792
Variance11576.22316
MonotonicityNot monotonic
2021-06-28T19:42:26.131613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.636686752
 
0.1%
27.392489272
 
0.1%
17.628961752
 
0.1%
34.631016952
 
0.1%
17.988421052
 
0.1%
36.046808512
 
0.1%
43.21922
 
0.1%
27.527764712
 
0.1%
3.6328813561
 
< 0.1%
8.2921568631
 
< 0.1%
Other values (2757)2757
99.4%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%

avg_recency_days
Real number (ℝ≥0)

Distinct305
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.42918919
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:26.328723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)65

Descriptive statistics

Standard deviation66.52989591
Coefficient of variation (CV)0.8482797871
Kurtosis3.700911137
Mean78.42918919
Median Absolute Deviation (MAD)30
Skewness1.833646582
Sum217641
Variance4426.22705
MonotonicityNot monotonic
2021-06-28T19:42:26.538239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3540
 
1.4%
7039
 
1.4%
5537
 
1.3%
3136
 
1.3%
4536
 
1.3%
2136
 
1.3%
2535
 
1.3%
2634
 
1.2%
4634
 
1.2%
3833
 
1.2%
Other values (295)2415
87.0%
ValueCountFrequency (%)
19
0.3%
25
0.2%
38
0.3%
48
0.3%
55
0.2%
ValueCountFrequency (%)
3661
< 0.1%
3651
< 0.1%
3641
< 0.1%
3631
< 0.1%
3572
0.1%

returns
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.124684685
Minimum0
Maximum45
Zeros1483
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:26.722961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.375741003
Coefficient of variation (CV)2.112361834
Kurtosis106.8386356
Mean1.124684685
Median Absolute Deviation (MAD)0
Skewness7.759467296
Sum3121
Variance5.644145313
MonotonicityNot monotonic
2021-06-28T19:42:26.895688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
01483
53.4%
1656
23.6%
2270
 
9.7%
3139
 
5.0%
492
 
3.3%
538
 
1.4%
632
 
1.2%
721
 
0.8%
98
 
0.3%
125
 
0.2%
Other values (13)31
 
1.1%
ValueCountFrequency (%)
01483
53.4%
1656
23.6%
2270
 
9.7%
3139
 
5.0%
492
 
3.3%
ValueCountFrequency (%)
451
< 0.1%
441
< 0.1%
351
< 0.1%
271
< 0.1%
211
< 0.1%

latitude
Real number (ℝ)

Distinct28
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.48388445
Minimum-25.274398
Maximum64.963051
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)0.3%
Memory size21.8 KiB
2021-06-28T19:42:27.063370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-25.274398
5-th percentile50.503887
Q155.378051
median55.378051
Q355.378051
95-th percentile55.378051
Maximum64.963051
Range90.237449
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.18198778
Coefficient of variation (CV)0.09511046858
Kurtosis164.9603122
Mean54.48388445
Median Absolute Deviation (MAD)0
Skewness-11.49946557
Sum151192.7794
Variance26.85299735
MonotonicityNot monotonic
2021-06-28T19:42:27.235837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
55.3780512515
90.6%
51.16569167
 
2.4%
46.22763858
 
2.1%
40.46366720
 
0.7%
50.50388719
 
0.7%
46.81818812
 
0.4%
39.39987211
 
0.4%
-25.2743988
 
0.3%
56.263927
 
0.3%
61.924117
 
0.3%
Other values (18)51
 
1.8%
ValueCountFrequency (%)
-25.2743988
0.3%
1.3520831
 
< 0.1%
31.0460511
 
< 0.1%
35.1264134
0.1%
35.9374961
 
< 0.1%
ValueCountFrequency (%)
64.9630511
 
< 0.1%
61.924117
0.3%
60.4720246
0.2%
60.1281614
0.1%
56.263927
0.3%

longitude
Real number (ℝ)

Distinct28
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.911966504
Minimum-106.346771
Maximum138.252924
Zeros0
Zeros (%)0.0%
Negative2552
Negative (%)92.0%
Memory size21.8 KiB
2021-06-28T19:42:27.366171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-106.346771
5-th percentile-3.435973
Q1-3.435973
median-3.435973
Q3-3.435973
95-th percentile8.227512
Maximum138.252924
Range244.599695
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.70437596
Coefficient of variation (CV)-5.598621071
Kurtosis134.0336741
Mean-1.911966504
Median Absolute Deviation (MAD)0
Skewness9.896905264
Sum-5305.707049
Variance114.5836646
MonotonicityNot monotonic
2021-06-28T19:42:27.491208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
-3.4359732515
90.6%
10.45152667
 
2.4%
2.21374958
 
2.1%
-3.7492220
 
0.7%
4.46993619
 
0.7%
8.22751212
 
0.4%
-8.22445411
 
0.4%
133.7751368
 
0.3%
9.5017857
 
0.3%
25.7481517
 
0.3%
Other values (18)51
 
1.8%
ValueCountFrequency (%)
-106.3467711
 
< 0.1%
-95.7128911
 
< 0.1%
-19.0208351
 
< 0.1%
-8.243893
 
0.1%
-8.22445411
0.4%
ValueCountFrequency (%)
138.2529245
0.2%
133.7751368
0.3%
103.8198361
 
< 0.1%
34.8516121
 
< 0.1%
33.4298594
0.1%

frequency_purchase
Real number (ℝ≥0)

SKEWED

Distinct1228
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06181163036
Minimum0.005464480874
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:27.621488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.005464480874
5-th percentile0.008771929825
Q10.01587301587
median0.02452316076
Q30.04215713428
95-th percentile0.1179152512
Maximum34
Range33.99453552
Interquartile range (IQR)0.02628411841

Descriptive statistics

Standard deviation0.6693187857
Coefficient of variation (CV)10.82836324
Kurtosis2387.960895
Mean0.06181163036
Median Absolute Deviation (MAD)0.01078689703
Skewness47.42232667
Sum171.5272742
Variance0.4479876369
MonotonicityNot monotonic
2021-06-28T19:42:27.764445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0714285714316
 
0.6%
0.0476190476215
 
0.5%
0.0158730158714
 
0.5%
0.030303030314
 
0.5%
0.0285714285714
 
0.5%
0.0238095238113
 
0.5%
0.0645161290313
 
0.5%
0.142857142913
 
0.5%
0.02512
 
0.4%
0.117647058812
 
0.4%
Other values (1218)2639
95.1%
ValueCountFrequency (%)
0.0054644808741
< 0.1%
0.0054794520551
< 0.1%
0.0054945054951
< 0.1%
0.0055096418731
< 0.1%
0.0056022408962
0.1%
ValueCountFrequency (%)
341
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
26
0.2%
1.51
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

Distinct974
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.48464482
Minimum1
Maximum297.8823529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-06-28T19:42:27.936904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q110.04545455
median17
Q327.33333333
95-th percentile54.215
Maximum297.8823529
Range296.8823529
Interquartile range (IQR)17.28787879

Descriptive statistics

Standard deviation17.84655749
Coefficient of variation (CV)0.8306656982
Kurtosis27.52701801
Mean21.48464482
Median Absolute Deviation (MAD)8
Skewness3.258241693
Sum59619.88938
Variance318.4996143
MonotonicityNot monotonic
2021-06-28T19:42:28.097184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1345
 
1.6%
1730
 
1.1%
1130
 
1.1%
1428
 
1.0%
126
 
0.9%
7.525
 
0.9%
1525
 
0.9%
925
 
0.9%
2324
 
0.9%
9.524
 
0.9%
Other values (964)2493
89.8%
ValueCountFrequency (%)
126
0.9%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
ValueCountFrequency (%)
297.88235291
< 0.1%
1911
< 0.1%
135.33333331
< 0.1%
129.751
< 0.1%
125.751
< 0.1%

Interactions

2021-06-28T19:41:52.843791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.005401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.119682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.242453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.361119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.653959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.828289image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:53.968038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.132327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.257742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.364230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.473640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.578719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.696464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.845379image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:54.970351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.078954image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.212694image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.328596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.443198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.557241image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.706101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:55.851859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.003539image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.149634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.304641image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.472824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.662242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.834610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:56.979345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:57.144916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:57.296057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:57.482718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:57.655715image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:57.835735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.004501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.142807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.288818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.413684image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.692332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.835314image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:58.977192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:59.118957image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:59.267410image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:59.465683image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:59.616438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:59.784607image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:41:59.907905image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.033189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.155670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.323367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.497882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.610540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.719611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.838387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:00.958486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.110110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.269252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.415967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.548636image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.680176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.815837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:01.965171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.112425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.236485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.369438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.561525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.714904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.843236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:02.970073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:03.102126image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:03.261831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:03.443168image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:03.585028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:03.746248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:03.909524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.047764image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.170727image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.284117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.594510image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.733626image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.851557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:04.968351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.092061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.230576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.351227image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.468221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.587082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.709724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.841668image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:05.966052image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.094847image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.236400image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.370904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.511319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.639485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.784804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:06.957387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.109301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.257035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.401221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.538767image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.700243image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.850124image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:07.981069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.113824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.251881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.415025image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.538388image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.658869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.784906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:08.930359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.073162image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.185503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.303974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.404782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.502817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.601943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.711274image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.840694image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:09.992049image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.132552image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.233690image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.347572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.448116image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.607586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.744032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.867021image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:10.982054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:11.300844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:11.439461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:11.569088image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:11.702341image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:11.837453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:11.964285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.087847image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.213814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.335251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.462747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.607031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.754829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:12.896892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.019040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.144579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.269789image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.421172image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.581766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.719376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:13.874249image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.016494image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.123702image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.224539image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.333524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.455444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.557497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.667158image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.786523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:14.897164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.005697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.116872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.232152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.345226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.461076image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.563381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.670767image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.771471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.881972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:15.996186image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.110708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.261969image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.396832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.520988image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.668407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.813012image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:16.977709image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:17.146394image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:17.337789image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:17.490925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:17.624219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:17.731897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:17.895035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.043544image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.161162image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.279468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.460033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.613573image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.751050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:18.889189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:19.020399image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:19.165091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:19.318933image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:19.423511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:19.786395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:19.912192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.022284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.134552image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.250971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.359773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.469822image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.579051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.691387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.821934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:20.946416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.065302image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.186408image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.306534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.430054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.572401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.744852image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-28T19:42:21.924066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-06-28T19:42:28.240665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-28T19:42:28.470524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-28T19:42:29.027654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-28T19:42:29.253200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-28T19:42:22.309612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-28T19:42:22.762750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsqtde_unique_productsavg_ticketavg_recency_daysreturnslatitudelongitudefrequency_purchaseavg_basket_size
00178505391.21372.034.01733.0297.021.018.1522221.01.055.378051-3.43597334.0000008.735294
11130473232.5956.09.01390.0171.0105.018.90403552.07.055.378051-3.4359730.02839119.000000
22125836705.382.015.05028.0232.0114.028.90250026.02.046.2276382.2137490.04043115.466667
3313748948.2595.05.0439.028.024.033.86607192.00.055.378051-3.4359730.0179865.600000
4415100876.00333.03.080.03.01.0292.00000020.03.055.378051-3.4359730.0750001.000000
55152914623.3025.014.02102.0102.061.045.32647126.05.055.378051-3.4359730.0402307.285714
66146885630.877.021.03621.0327.0148.017.21978619.06.055.378051-3.4359730.05737715.285714
77178095411.9116.012.02057.061.046.088.71983639.02.055.378051-3.4359730.0336135.083333
881531160767.900.091.038194.02379.0567.025.5434644.027.055.378051-3.4359730.24396825.901099
99160982005.6387.07.0613.067.034.029.93477647.00.055.378051-3.4359730.0244769.428571

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsqtde_unique_productsavg_ticketavg_recency_daysreturnslatitudelongitudefrequency_purchaseavg_basket_size
2765560917290525.243.02.0404.0102.092.05.14941213.00.055.378051-3.4359730.15384647.000000
276656181478577.4010.02.084.03.02.025.8000005.00.055.378051-3.4359730.4000001.500000
2767561917254272.444.02.0252.0112.0100.02.43250011.00.055.378051-3.4359730.18181855.500000
2768563517232421.522.02.0203.036.030.011.70888912.00.055.378051-3.4359730.16666717.500000
2769563617468137.0010.02.0116.05.05.027.4000004.00.055.378051-3.4359730.5000002.500000
2770564713596697.045.02.0406.0166.0133.04.1990367.00.055.378051-3.4359730.28571469.000000
27715653148931237.859.02.0799.073.072.016.9568492.00.055.378051-3.4359731.00000036.500000
2772567814126706.137.03.0508.015.014.047.0753333.01.055.378051-3.4359731.0000005.000000
27735684135211092.391.03.0733.0435.0312.02.5112414.00.055.378051-3.4359730.333333135.333333
2774569415060301.848.04.0262.0120.080.02.5153331.00.055.378051-3.4359734.00000027.500000